Intelligent Intrusion Detection System using Decision Tree Classifier and Bootstrap Aggregation

Detection System ◽

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Tree Classifier

The exponential growth in the use of computers over networks, as well as the proliferation of applications that operate on different platforms, has drawn attention to network security. This paradigm takes advantage of security flaws in all operating systems that are both technically difficult and costly to fix. As a result, intrusion is used as a key to worldwide a computer resource's credibility, availability, and confidentiality. The Intrusion Detection System (IDS) is critical in detecting network anomalies and attacks. In this paper, the data mining principle is combined with IDS to efficiently and quickly identify important, secret data of interest to the user. The proposed algorithm addresses four issues: data classification, high levels of human interaction, lack of labeled data, and the effectiveness of distributed denial of service attacks. We're also working on a decision tree classifier that has a variety of parameters. The previous algorithm classified IDS up to 90% of the time and was not appropriate for large data sets. Our proposed algorithm was designed to accurately classify large data sets. Aside from that, we quantify a few more decision tree classifier parameters.

Hybrid Intrusion Detection System Based on the Stacking Ensemble of C5 Decision Tree Classifier and One Class Support Vector Machine

Electronics ◽

10.3390/electronics9010173 ◽

2020 ◽

Vol 9 (1) ◽

pp. 173 ◽

Cited By ~ 9

Author(s):

Ansam Khraisat ◽

Iqbal Gondal ◽

Peter Vamplew ◽

Joarder Kamruzzaman ◽

Ammar Alazab

Keyword(s):

Support Vector Machine ◽

Intrusion Detection ◽

Decision Tree ◽

False Alarm ◽

Detection System ◽

Support Vector ◽

Tree Classifier ◽

False Alarm Rates

Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates.

An intelligent intrusion detection system using genetic based feature selection and Modified J48 decision tree classifier

2013 Fifth International Conference on Advanced Computing (ICoAC) ◽

10.1109/icoac.2013.6921918 ◽

2013 ◽

Cited By ~ 6

Author(s):

B. Senthilnayaki ◽

K. Venkatalakshmi ◽

A. Kannan

Keyword(s):

Feature Selection ◽

Intrusion Detection ◽

Decision Tree ◽

Detection System ◽

Tree Classifier ◽

J48 Decision Tree

Lecture Notes in Computer Science - Trends and Applications in Knowledge Discovery and Data Mining ◽

An Anomaly Intrusion Detection System Using C5 Decision Tree Classifier

10.1007/978-3-030-04503-6_14 ◽

2018 ◽

pp. 149-155 ◽

Cited By ~ 2

Author(s):

Ansam Khraisat ◽

Iqbal Gondal ◽

Peter Vamplew

Keyword(s):

Intrusion Detection ◽

Decision Tree ◽

Detection System ◽

Tree Classifier ◽

Anomaly Intrusion Detection

Performance Evaluation of Intrusion Detection System using Selected Features and Machine Learning Classifiers

Baghdad Science Journal ◽

10.21123/bsj.2021.18.2(suppl.).0884 ◽

2021 ◽

Vol 18 (2(Suppl.)) ◽

pp. 0884

Author(s):

Raja Azlina Raja Mahmood ◽

AmirHossien Abdi ◽

Masnida Hussin

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Intrusion Detection ◽

Decision Tree ◽

Network Traffic ◽

Detection System ◽

Support Vector ◽

Large Network ◽

Tree Classifier

Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems. Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic. Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance. In this study, two different sets of selected features have been adopted to train four machine-learning based classifiers. The two sets of selected features are based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) approach respectively. These evolutionary-based algorithms are known to be effective in solving optimization problems. The classifiers used in this study are Naïve Bayes, k-Nearest Neighbor, Decision Tree and Support Vector Machine that have been trained and tested using the NSL-KDD dataset. The performance of the abovementioned classifiers using different features values was evaluated. The experimental results indicate that the detection accuracy improves by approximately 1.55% when implemented using the PSO-based selected features than that of using GA-based selected features. The Decision Tree classifier that was trained with PSO-based selected features outperformed other classifiers with accuracy, precision, recall, and f-score result of 99.38%, 99.36%, 99.32%, and 99.34% respectively. The results show that using optimal features coupling with a good classifier in a detection system able to reduce the classifier model building time, reduce the computational burden to analyze data, and consequently attain high detection rate.